Open access peer-reviewed Edited Volume

Bayesian Inference

Niansheng Tang

Yunnan University

Niansheng Tang is a member of the International Statistical Institute (ISI), a member of the board of the International Chinese Statistical Association (ICSA), and IMS (Institute of Mathematical Statistics) Fellow. He serves as a member of the editorial board for Statistics and Its Interface, Journal of Systems Science and Complexity, BMC Medical Research Methodology. He is also an Editor in Field for Communications in Mathematics and Statistics.


Variational Bayes Empirical Bayes Objective Bayes Objective Prior Bayesian Classification Bayesian Discrimination Bayesian Clustering High-Dimensional Data Bayesian Network Deep Learning Machine Learning Reinforcement Learning

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About the book

This book aims to introduce the recent developments on theories, methods, algorithms, and applications of Bayesian inference, including variational Bayesian estimation, Bayesian variable selection or model selection, empirical Bayesian inference, objective Bayesian inference, Bayesian classification or clustering or discrimination, Bayesian deep learning and Bayesian reinforcement learning, robust Bayesian inference, Bayesian case deletion influence analysis, and Bayesian local influence analysis. Other covered topics will include Bayesian sensitivity analysis for high dimensional or ultrahigh dimensional complicated data such as categorical, ordered, and continuous data as well as imaging data, text data, repeated measurement data, or longitudinal data, or panel data, dynamic data, missing data, and so on. Various models such as probabilistic graphical models, generalized estimating equations, generalized moment models, parametric or nonparametric or semiparametric models, latent variable models, multivariate multilevel models, hidden Markov models, dynamic factor models, tensor regression models are also welcome. In particular, this book intends to demonstrate the application of Bayesian inference methods to artificial intelligence, medical imaging analysis, and topological data analysis, among others.

Publishing process

Book initiated and editor appointed

Date completed: December 1st 2021

Applications to edit the book are assessed and a suitable editor is selected, at which point the process begins.

Chapter proposals submitted and reviewed

Deadline Extended: Open for Submissions

Potential authors submit chapter proposals ready for review by the academic editor and our publishing review team.

Approved chapters written in full and submitted

Deadline for full chapters: February 27th 2022

Once approved by the academic editor and publishing review team, chapters are written and submitted according to pre-agreed parameters

Full chapters peer reviewed

Review results due: May 18th 2022

Full chapter manuscripts are screened for plagiarism and undergo a Main Editor Peer Review. Results are sent to authors within 30 days of submission, with suggestions for rounds of revisions.

Book compiled, published and promoted

Expected publication date: July 17th 2022

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About the editor

Niansheng Tang

Yunnan University

Niansheng Tang is a Professor of Statistics and Dean of the School of Mathematics and Statistics, Yunnan University. He was elected a Yangtze River Scholars Distinguished Professor in 2013, a member of the International Statistical Institute (ISI) in 2016, a member of the board of International Chinese Statistical Association (ICSA) in 2018, and IMS (Institute of Mathematical Statistics) Fellow in 2021. He obtained the National Science Foundation for Distinguished Young Scholars of China in 2012 and was awarded the Outstanding Service Award of the ICSA in 2018. He serves as a member of the editorial board for Statistics and Its Interface, Journal of Systems Science and Complexity, BMC Medical Research Methodology. He is also an Editor in Field for Communications in Mathematics and Statistics. His research interests include data clustering, biostatistics, Bayesian statistics, missing data analysis, statistical diagnosis, variable selection, high-dimensional data analysis, tensor data analysis, differential privacy, deep learning. He has published more than 175 research papers and authored four books.

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